Traffic flow rates

ABSTRACT

One or more techniques and/or systems are provided for determining a scaled flow rate of traffic for a road segment. For example, probe flow rate information is determined based upon locational information from one or more probe vehicles on a road segment (e.g., a flow rate of probe vehicles corresponding to a sum of probe vehicles identified from time stamped global positioning system coordinates provided by the probe vehicles). Satellite imagery of the road segment is analyzed to identify a count of vehicles on the road segment. Scale factor and offset information is estimated based upon the probe flow rate information and the count of vehicles. The scale factor and offset information is used to scale the probe flow rate information to determine a scaled flow rate that may be a relatively accurate flow rate of traffic, which may correspond to an inferred traffic volume along the road segment.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to and is a continuation of U.S.application Ser. No. 15/123,244, filed on Sep. 1, 2016, entitled“TRAFFIC FLOW RATES” and U.S. Provisional Patent Application No.61/946,962 titled “DETERMINING HOV/HOT LANE TRAVEL TIMES”, filed on Mar.3, 2014, which are hereby incorporated by reference.

BACKGROUND

Many users utilize various devices to obtain route information from aroute provider. In an example, a user may utilize a smart phone toobtain driving directions to a nearby restaurant. In another example, auser may utilize a vehicle navigation device to obtain a map populatedwith driving directions to an amusement park. The route provider may beable to provide relatively more accurate and efficient routes to usersif the route provider has information relating to traffic volumes, flowrates, congestion, accidents, traffic obstructions, etc. Traffic volumeand flow rates may be identified from probe flow rate informationderived from locational information, from probe vehicles, such as timestamped global positioning system (GPS) coordinates. Unfortunately, theprobe flow rate information may merely represent a small portion of theactual vehicles on the road. For example, less than 2% of the vehiclesmay provide probe flow rate information for a road segment, and thus theprobe flow rate information may need to be scaled to the total amount oftraffic. However, the total amount of traffic may be unknown, and thusthe scale factor may be imprecise. If relatively more accurate trafficvolume and flow rate information could be identified, then cityplanning, measurement of business activity, the flow of demographicgroups, travel route planning, and/or other information may be moreaccurately determined.

SUMMARY

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the detaileddescription. This summary is not intended to identify key factors oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

Among other things, one or more systems and/or techniques fordetermining a scaled flow rate of traffic for a road segment areprovided herein. Probe flow rate information may be determined basedupon locational information from one or more probe vehicles on a roadsegment (e.g., a count of probe vehicles on the road segment). Forexample, the probe flow rate information may be derived from locationalinformation, such as global positioning system (GPS) coordinates andtimestamps, received from the one or more probe vehicles. Satelliteimagery, such as an image or a video, of the road segment may beobtained. The satellite imagery may be analyzed to identify a count ofvehicles on the road segment for a unit of time (e.g., linear featuresand/or a road network overlaid the satellite imagery may be used toidentify the road segment, and parallelograms or other shape featuresmay be used to identify vehicles).

Scale factor and offset information may be estimated based upon theprobe flow rate information and the count of vehicles for the unit oftime. The probe flow rate information may be scaled based upon the scalefactor and offset information to determine a scaled flow rate (e.g., 3probe vehicles may have provided locational information for the roadsegment and 30 vehicles may have been counted for the road segment, andthus the probe flow rate may be scaled by 10).

To the accomplishment of the foregoing and related ends, the followingdescription and annexed drawings set forth certain illustrative aspectsand implementations. These are indicative of but a few of the variousways in which one or more aspects may be employed. Other aspects,advantages, and novel features of the disclosure will become apparentfrom the following detailed description when considered in conjunctionwith the annexed drawings.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow diagram illustrating an exemplary method of determininga scaled flow rate of traffic for a road segment.

FIG. 2A is a component block diagram illustrating an exemplary systemfor determining a scaled flow rate of traffic for a road segment, whereprobe flow rate information is obtained.

FIG. 2B is a component block diagram illustrating an exemplary systemfor determining a scaled flow rate of traffic for a road segment, wherescale factor and offset information is estimated.

FIG. 2C is a component block diagram illustrating an exemplary systemfor determining a scaled flow rate of traffic for a road segment, whereprobe flow rate information is scaled based upon scale factor and offsetinformation to determine a scaled flow rate.

FIG. 3 is a component block diagram illustrating an exemplary system fordetermining a real-time flow rate of traffic for a road segment.

FIG. 4 is an illustration of an exemplary computer readable mediumwherein processor-executable instructions configured to embody one ormore of the provisions set forth herein may be comprised.

FIG. 5 illustrates an exemplary computing environment wherein one ormore of the provisions set forth herein may be implemented.

DETAILED DESCRIPTION

The claimed subject matter is now described with reference to thedrawings, wherein like reference numerals are generally used to refer tolike elements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth to provide anunderstanding of the claimed subject matter. It may be evident, however,that the claimed subject matter may be practiced without these specificdetails. In other instances, structures and devices are illustrated inblock diagram form in order to facilitate describing the claimed subjectmatter.

One or more systems and/or techniques for determining a scaled flow rateof traffic for a road segment are provided herein. Traffic volume andflow rate information may be useful for city planning, measuringbusiness activity, tracking the flow of demographic groups betweenlocales, generating travel routes for users, and/or a variety of otheruses. Unfortunately, determining a flow rate by scaling probe flow rateinformation, derived from locational information provided by probevehicles, based upon an estimate of the percentage of traffic that suchprobe vehicles represent may be inaccurate because the percentage may beunknown and thus an estimated guess may be used (e.g., the number ofprobe vehicles providing locational information, such as globalpositioning system (GPS) coordinates, used to determine a probe flowrate may merely represent 2% or less of the actual traffic along a roadsegment). Road sensors may be used to obtain flow rates, however, roadsensors may not be located along road segments of interest and may notprovide lane counts or other relatively accurate information.Accordingly, as provided herein, satellite imagery may be used toidentify a count of vehicles on a road segment. The count of vehiclesand probe flow rate information may be used to estimate scale factor andoffset information that may be used to scale probe flow rates todetermine scaled flow rates. Scaled flow rates may be relatively moreaccurate indicators of traffic volumes and flow rates than merelyscaling probe flow rates using estimated guesses of scaling factorsbecause the scale flow rates are derived from relatively accurate countsof vehicles. In this way, relatively more accurate information oftraffic volumes and flow rates may be used to provide more accurate andefficient travel routes, city planning, measurements of businessactivity, flow of demographic groups, and/or other use cases of trafficvolume and flow rate information.

An embodiment of determining a scaled flow rate of traffic for a roadsegment is illustrated by an exemplary method 100 of FIG. 1. At 102, themethod 100 starts. At 104, probe flow rate information may be determinedbased upon locational information from one or more probe vehicles on aroad segment (e.g., a count of probe vehicles on the road segment). Forexample, the probe flow rate information may be derived from globalpositing system (GPS) coordinates and timestamp information provided byvehicle head units (e.g., a navigation device requesting a drivingroute), a mobile device (e.g., a map application hosted by a smartphoneof a driver), a vehicle computing device, and/or other devices locatedwithin a vehicle. In an example, 3 probe vehicles may provide GPScoordinates and timestamp information while traveling the road segment.

At 106, satellite imagery (e.g., an image, video, etc.) of the roadsegment may be obtained. At 108, the satellite imagery may be analyzedto identify a count of vehicles on the road segment for a unit of time.In an example, the road segment may be identified within the satelliteimagery based upon a linear feature indicative of the road segment. Inanother example, the road segment may be identified within the satelliteimagery by overlaying a road network from a map onto the satelliteimagery. In an example, a vehicle may be identified based upon anidentification of a parallelogram or other shape within the satelliteimagery. In another example, an image analysis algorithm may be utilizedto identify the count of vehicles. The image analysis algorithm, such asan edge detection function or a threshold used by the image analysisalgorithm, may be adjusted based upon the probe flow rate informationand/or the locational information (e.g., locations of the 3 probevehicles may be known, and thus the 3 probe vehicles may be identifiedwithin the satellite imagery based upon the locational information,which may be used as feedback to adjust the image analysis algorithm todetect other vehicles). In an example, 100 vehicles, including the 3probe vehicles, may be identified as traveling along the road segment.

At 110, scale factor and offset information may be estimated based uponthe probe flow rate information and the count of vehicles for the unitof time. For example, the scale factor and offset information may bederived from the knowledge that the 3 probe vehicles represented 3% ofthe 100 vehicles on the road segment. In an example, the scale factorand offset information may be estimated based upon the probe flow rateinformation and a ground flow rate determined by the count of vehicles.In an example, the scale factor and offset information may be averagedbased upon a road type of the road segment, a day of the week, a numberof lanes of the road segment, a season (e.g., winter drivingconditions), road construction, a weather condition, and/or a variety ofother factors.

At 112, the probe flow rate information may be scaled based upon thescale factor and offset information to determine a scaled flow rate. Forexample, a probe flow rate, as determined by the GPS coordinates andtimestamps from the 3 probe vehicles, may be scaled to the 100 totalvehicles to determine the scaled flow rate. The scaled flow rate maycorrespond to inferred traffic volume along the road segment. In anexample, the scaled flow rate may be determined for a second unit oftime, different than the unit of the time of the satellite imagery, inreal-time based upon the probe flow rate information corresponding toreal-time locational information of the one or more probe vehicles(e.g., the scale factor and offset information may be used to determinescaled flow rates for subsequent times, days, weeks, etc. such as inreal-time). For example, real-time locational information (e.g., GPScoordinates and/or timestamps used to identify probe flow information)may be obtained from a set of probe vehicles traveling along the roadsegment (e.g., real-time locational information obtained a week afterthe scale factor and offset information was determined). The scalefactor and offset information may be applied to the real-time locationalinformation to determine a real-time flow rate for the road segment. Inan example, the scale factor and offset information may be used withvehicle speed information and the probe flow rate information (e.g., avolume of probe vehicles on the road segment), to determine a trafficdensity. At 114, the method 100 ends.

FIGS. 2A-2C illustrate examples of a system 200, comprising a trafficmodeling component 212, for determining a scaled flow rate of trafficfor a road segment 202. The traffic modeling component 212 may establishcommunication connections with one or more probe vehicles, such as afirst probe vehicle 204 and a second probe vehicle 206, traveling alongthe road segment 202. The traffic modeling component 212 may receivefirst time stamped locational information 208 (e.g., GPS coordinates)from the first probe vehicle 204, as illustrated in FIG. 2A. The trafficmodeling component 212 may receive second time stamped locationalinformation 210 from the second probe vehicle 206.

Probe flow traffic rate information 214 may be derived from the firsttime stamped locational information 208 and the second time stampedlocational information 210. Because the probe flow rate information 214may merely represent a fraction of the vehicles on the road segment 202,the probe flow rate information 214 will be scaled by a scale factorcorresponding to the fraction of the traffic that the first probevehicle 204 and the second probe vehicle 206 represent (e.g., the 2probe vehicles and 78 non-probe vehicles may be traveling the roadsegment 202). Accordingly, as provided herein, a count of vehicles onthe road segment 202 may be identified from satellite imagery in orderto estimate relatively accurate scale factor and offset information tothe apply to the probe flow rate information 214 (e.g., as opposed tomerely estimating/guessing a number of vehicles on the road segment202).

FIG. 2B illustrates an example of the traffic modeling component 212obtaining satellite imagery 220. The traffic modeling component 212 mayanalyze the satellite imagery 220, such as using an image analysisalgorithm, linear feature extraction, and/or overlaying a road networkof a map onto the satellite imagery 220, to identify the road segment202. The traffic modeling component 212 may analyze the satelliteimagery 220, such as using the image analysis algorithm and/or shapefeature extraction (e.g., identification of parallelograms indicative ofvehicles), to identify a count of vehicles 222 on the road segment 202(e.g., 80 vehicles corresponding to the 2 probe vehicles and the 78non-probe vehicles). The traffic modeling component 212 may estimatescale factor and offset information 224 based upon the probe flow rateinformation 214 and the count of vehicles 222 on the road segment 202.The scale factor and offset information 224 may comprising scalingand/or offset values used to scale the probe flow rate information 214(e.g., a probe flow rate) from the 2 probe vehicles to a scale flow ratefor the 80 total vehicles. FIG. 2C illustrates the traffic modelingcomponent 212 scaling the probe flow rate information 214 based upon thescale factor and offset information 224 to determine a scaled flow rate226 for the road segment 202 (e.g., flow rate of the 2 probe vehiclesmay be scaled to a flow rate of the 80 total vehicles on the roadsegment 202).

FIG. 3 illustrates an example of a system 300, comprising a trafficmodeling component 308, for determining a real-time flow rate 312 (e.g.,a scaled flow rate) of a road segment 302 (e.g., a road segmentcorresponding to road segment 202 of FIGS. 2A-2C). The traffic modelingcomponent 308 may obtain real-time locational information 306 from a setof probe vehicles, such as a probe vehicle 304, traveling along the roadsegment 302. The traffic modeling component 308 may maintain scalefactor and offset information 310 for the road segment 302 (e.g., scalefactor and offset information 224 of FIG. 2B). The traffic modelingcomponent 308 may apply the scale factor and offset information 310 tothe real-time locational information 306 to determine a real-time flowrate 312 for the road segment 302. In this way, real-time flow rates maybe estimated for the road segment 302 by applying the scale factor andoffset information 310 to real-time locational information.

Still another embodiment involves a computer-readable medium comprisingprocessor-executable instructions configured to implement one or more ofthe techniques presented herein. An example embodiment of acomputer-readable medium or a computer-readable device is illustrated inFIG. 4, wherein the implementation 400 comprises a computer-readablemedium 408, such as a CD-R, DVD-R, flash drive, a platter of a hard diskdrive, etc., on which is encoded computer-readable data 406. Thiscomputer-readable data 406, such as binary data comprising at least oneof a zero or a one, in turn comprises a set of computer instructions 404configured to operate according to one or more of the principles setforth herein. In some embodiments, the set of computer instructions 404are configured to perform a method 402, such as at least some of theexemplary method 100 of FIG. 1, for example. In some embodiments, theset of computer instructions 404 are configured to implement a system,such as at least some of the exemplary system 200 of FIGS. 2A-2C and/orat least some of the exemplary system 300 of FIG. 3, for example. Manysuch computer-readable media are devised by those of ordinary skill inthe art that are configured to operate in accordance with the techniquespresented herein.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing at least some of the claims.

As used in this application, the terms “component,” “module,” “system”,“interface”, and/or the like are generally intended to refer to acomputer-related entity, either hardware, a combination of hardware andsoftware, software, or software in execution. For example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution, a program,and/or a computer. By way of illustration, both an application runningon a controller and the controller can be a component. One or morecomponents may reside within a process and/or thread of execution and acomponent may be localized on one computer and/or distributed betweentwo or more computers.

Furthermore, the claimed subject matter may be implemented as a method,apparatus, or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware, or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device, carrier, or media. Of course, manymodifications may be made to this configuration without departing fromthe scope or spirit of the claimed subject matter.

FIG. 5 and the following discussion provide a brief, general descriptionof a suitable computing environment to implement embodiments of one ormore of the provisions set forth herein. The operating environment ofFIG. 5 is only one example of a suitable operating environment and isnot intended to suggest any limitation as to the scope of use orfunctionality of the operating environment. Example computing devicesinclude, but are not limited to, personal computers, server computers,hand-held or laptop devices, mobile devices (such as mobile phones,Personal Digital Assistants (PDAs), media players, and the like),multiprocessor systems, consumer electronics, mini computers, mainframecomputers, distributed computing environments that include any of theabove systems or devices, and the like.

Although not required, embodiments are described in the general contextof “computer readable instructions” being executed by one or morecomputing devices. Computer readable instructions may be distributed viacomputer readable media (discussed below). Computer readableinstructions may be implemented as program modules, such as functions,objects, Application Programming Interfaces (APIs), data structures, andthe like, that perform particular tasks or implement particular abstractdata types. Typically, the functionality of the computer readableinstructions may be combined or distributed as desired in variousenvironments.

FIG. 5 illustrates an example of a system 500 comprising a computingdevice 512 configured to implement one or more embodiments providedherein. In one configuration, computing device 512 includes at least oneprocessing unit 516 and memory 518. Depending on the exact configurationand type of computing device, memory 518 may be volatile (such as RAM,for example), non-volatile (such as ROM, flash memory, etc., forexample) or some combination of the two. This configuration isillustrated in FIG. 5 by dashed line 514.

In other embodiments, device 512 may include additional features and/orfunctionality. For example, device 512 may also include additionalstorage (e.g., removable and/or non-removable) including, but notlimited to, magnetic storage, optical storage, and the like. Suchadditional storage is illustrated in FIG. 5 by storage 520. In oneembodiment, computer readable instructions to implement one or moreembodiments provided herein may be in storage 520. Storage 520 may alsostore other computer readable instructions to implement an operatingsystem, an application program, and the like. Computer readableinstructions may be loaded in memory 518 for execution by processingunit 516, for example.

The term “computer readable media” as used herein includes computerstorage media. Computer storage media includes volatile and nonvolatile,removable and non-removable media implemented in any method ortechnology for storage of information such as computer readableinstructions or other data. Memory 518 and storage 520 are examples ofcomputer storage media. Computer storage media includes, but is notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, Digital Versatile Disks (DVDs) or other optical storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other medium which can be used to storethe desired information and which can be accessed by device 512.Computer storage media does not, however, include propagated signals.Rather, computer storage media excludes propagated signals. Any suchcomputer storage media may be part of device 512.

Device 512 may also include communication connection(s) 526 that allowsdevice 512 to communicate with other devices. Communicationconnection(s) 526 may include, but is not limited to, a modem, a NetworkInterface Card (NIC), an integrated network interface, a radio frequencytransmitter/receiver, an infrared port, a USB connection, or otherinterfaces for connecting computing device 512 to other computingdevices. Communication connection(s) 526 may include a wired connectionor a wireless connection. Communication connection(s) 526 may transmitand/or receive communication media.

The term “computer readable media” may include communication media.Communication media typically embodies computer readable instructions orother data in a “modulated data signal” such as a carrier wave or othertransport mechanism and includes any information delivery media. Theterm “modulated data signal” may include a signal that has one or moreof its characteristics set or changed in such a manner as to encodeinformation in the signal.

Device 512 may include input device(s) 524 such as keyboard, mouse, pen,voice input device, touch input device, infrared cameras, video inputdevices, and/or any other input device. Output device(s) 522 such as oneor more displays, speakers, printers, and/or any other output device mayalso be included in device 512. Input device(s) 524 and output device(s)522 may be connected to device 512 via a wired connection, wirelessconnection, or any combination thereof. In one embodiment, an inputdevice or an output device from another computing device may be used asinput device(s) 524 or output device(s) 522 for computing device 512.

Components of computing device 512 may be connected by variousinterconnects, such as a bus. Such interconnects may include aPeripheral Component Interconnect (PCI), such as PCI Express, aUniversal Serial Bus (USB), firewire (IEEE 1394), an optical busstructure, and the like. In another embodiment, components of computingdevice 512 may be interconnected by a network. For example, memory 518may be comprised of multiple physical memory units located in differentphysical locations interconnected by a network.

Those skilled in the art will realize that storage devices utilized tostore computer readable instructions may be distributed across anetwork. For example, a computing device 530 accessible via a network528 may store computer readable instructions to implement one or moreembodiments provided herein. Computing device 512 may access computingdevice 530 and download a part or all of the computer readableinstructions for execution. Alternatively, computing device 512 maydownload pieces of the computer readable instructions, as needed, orsome instructions may be executed at computing device 512 and some atcomputing device 530.

Various operations of embodiments are provided herein. In oneembodiment, one or more of the operations described may constitutecomputer readable instructions stored on one or more computer readablemedia, which if executed by a computing device, will cause the computingdevice to perform the operations described. The order in which some orall of the operations are described should not be construed as to implythat these operations are necessarily order dependent. Alternativeordering will be appreciated by one skilled in the art having thebenefit of this description. Further, it will be understood that not alloperations are necessarily present in each embodiment provided herein.Also, it will be understood that not all operations are necessary insome embodiments.

Further, unless specified otherwise, “first,” “second,” and/or the likeare not intended to imply a temporal aspect, a spatial aspect, anordering, etc. Rather, such terms are merely used as identifiers, names,etc. for features, elements, items, etc. For example, a first object anda second object generally correspond to object A and object B or twodifferent or two identical objects or the same object.

Moreover, “exemplary” is used herein to mean serving as an example,instance, illustration, etc., and not necessarily as advantageous. Asused herein, “or” is intended to mean an inclusive “or” rather than anexclusive “or”. In addition, “a” and “an” as used in this applicationare generally be construed to mean “one or more” unless specifiedotherwise or clear from context to be directed to a singular form. Also,at least one of A and B and/or the like generally means A or B and/orboth A and B. Furthermore, to the extent that “includes”, “having”,“has”, “with”, and/or variants thereof are used in either the detaileddescription or the claims, such terms are intended to be inclusive in amanner similar to the term “comprising”.

Also, although the disclosure has been shown and described with respectto one or more implementations, equivalent alterations and modificationswill occur to others skilled in the art based upon a reading andunderstanding of this specification and the annexed drawings. Thedisclosure includes all such modifications and alterations and islimited only by the scope of the following claims. In particular regardto the various functions performed by the above described components(e.g., elements, resources, etc.), the terms used to describe suchcomponents are intended to correspond, unless otherwise indicated, toany component which performs the specified function of the describedcomponent (e.g., that is functionally equivalent), even though notstructurally equivalent to the disclosed structure. In addition, while aparticular feature of the disclosure may have been disclosed withrespect to only one of several implementations, such feature may becombined with one or more other features of the other implementations asmay be desired and advantageous for any given or particular application.

What is claimed is:
 1. A system for determining a flow rate of trafficfor a road segment, comprising: a traffic modeling component,implemented via a processor, configured to: determine probe flow rateinformation based upon locational information from one or more probevehicles on a road segment; obtain satellite imagery of the roadsegment; analyze the satellite imagery to identify a count of vehicleson the road segment for a unit of time; and scale the probe flow rateinformation based upon the count of vehicles for the unit of time todetermine a flow rate.
 2. The system of claim 1, the traffic modelingcomponent configured to: identify a linear feature for identifying theroad segment within the satellite imagery.
 3. The system of claim 1, thetraffic modeling component configured to: overlay a road network ontothe satellite imagery for identifying the road segment within thesatellite imagery.
 4. The system of claim 1, the traffic modelingcomponent configured to: identify a parallelogram for identifying avehicle within the satellite imagery.
 5. The system of claim 1, thetraffic modeling component configured to: determine a ground flow ratebased upon the count of vehicles; and determine the flow rate based uponthe probe flow rate information and the ground flow rate.
 6. The systemof claim 1, the traffic modeling component configured to: determine theflow rate for a second unit of time, different than the unit of time, inreal-time based upon the probe flow rate information corresponding toreal-time locational information of the one or more probe vehicles. 7.The system of claim 1, the traffic modeling component configured to:obtain real-time locational information from a set of probe vehiclestraveling along the road segment; and determine a real-time flow ratefor the road segment based upon the real-time locational information. 8.The system of claim 1, the locational information corresponding to atimestamp and global positioning system (GPS) coordinates of at leastone of a vehicle head unit, a vehicle computing device, or a mobiledevice located within a vehicle.
 9. The system of claim 1, the trafficmodeling component configured to: estimate scale factor and offsetinformation based upon the probe flow rate information and the count ofvehicles for the unit of time; and average the scale factor and offsetinformation based upon at least one of a road type, a day of week, anumber of lanes per road, a season, road construction, or a weathercondition.
 10. The system of claim 1, the traffic modeling componentconfigured to: utilize vehicle speed information and the probe flow rateinformation to determine a traffic density.
 11. The system of claim 1,the flow rate corresponding to inferred traffic volume along the roadsegment.
 12. The system of claim 1, the traffic modeling componentconfigured to: utilize an image analysis algorithm to identify the countof vehicles from the satellite imagery.
 13. The system of claim 12, thetraffic modeling component configured to: utilize the locationalinformation to identify one or more identified probe vehicles on theroad segment; and adjust the image analysis algorithm based upon the oneor more identified probe vehicles.
 14. The system of claim 13, thetraffic modeling component configured to: adjust at least one of an edgedetection function or a threshold used by the image analysis algorithmbased upon the one or more identified probe vehicles.
 15. Acomputer-implemented method for determining a scaled flow rate oftraffic for a road segment, comprising: determining probe flow rateinformation based upon locational information from one or more probevehicles on a road segment; obtaining imagery of the road segment;analyzing the imagery to identify a count of vehicles on the roadsegment for a unit of time; estimating scale factor and offsetinformation based upon the probe flow rate information and the count ofvehicles for the unit of time; and scaling, via a processor, the probeflow rate information based upon the scale factor and offset informationto determine a scaled flow rate.
 16. The computer-implemented method ofclaim 15, comprising: obtaining real-time locational information from aset of probe vehicles traveling along the road segment; and applying thescale factor and offset information to the real-time locationalinformation to determine a real-time flow rate for the road segment. 17.The computer-implemented method of claim 15, comprising: utilizing animage analysis algorithm to identify the count of vehicles from theimagery; utilizing the locational information to identify one or moreidentified probe vehicles on the road segment; and adjusting the imageanalysis algorithm based upon the one or more identified probe vehicles.18. The computer-implemented method of claim 15, the imagery comprisingan image.
 19. The computer-implemented method of claim 15, the imagerycomprising a video.
 20. A non-transitory computer readable mediumcomprising instructions which when executed perform a method fordetermining a scaled flow rate of traffic for a road segment,comprising: determining flow rate information associated with a roadsegment; obtaining satellite imagery of a road segment; analyzing thesatellite imagery to identify a count of vehicles on the road segmentfor a unit of time; estimating scale factor and offset information basedupon the flow rate information and the count of vehicles for the unit oftime; and scaling, via a processor, the flow rate information based uponthe scale factor and offset information to determine a scaled flow rate.